This invention relates generally to the field of cloud based video detection and tracking systems. More specifically, this disclosure presents the first attempt at detecting and tracking multiple moving targets from an airborne video within the framework of a cloud computing infrastructure for the application of suspicious behavior recognition.
The detection and tracking of moving objects is critical in many defense and security applications, where motion detection is usually performed in a preprocessing step, a key success in the following of a target tracking and automatic target recognition. Many videos used in defense and security applications are outdoor videos whose quality may be degraded by various noisy sources, such as atmospheric turbulence, sensor platform scintillation, etc. Meanwhile, moving objects may be very small occupying a few pixels only, which makes motion detection very challenging. Under this circumstance, existing approaches may generate significant amount of false alarms of detecting things that are not targets.
Motion detection has been extensively investigated. Many research works are conducted from indoor videos with large objects. As one of the major techniques, optical flow based approaches have been widely used for motion detection. There are two classic methods of optical flow computation in computer vision: Gunnar Farneback (GF) method and Lucas-Kanade (LK) method. Both of them are based on the two-frame differential algorithms. Since the LK method needs the construction of a pyramid model in sparse feature scale space and iterative computational updating at successively finer scales, a preferred approach would be to focus on the GF method for a dense optical flow computation.